The present invention relates to information processing technology and, in particular, to a technique to predict change-points in continuous time-series data.
Various techniques to detect change-points (transition points, inflection points) in continuous time-series data have been proposed. Considering industrial applicability, detecting (predicting) change-points in advance may be more advantageous than detecting change-points after they have occurred. In a hybrid motor vehicle, which includes both of an electricity-driven motor and a gasoline engine driven by fossil fuel, for example, various kinds of time-series data (such as the throttle opening angle, the amount of fuel supply to the engine, the speed of the motor vehicle, and acceleration, for example) are available from various sensors. If a symptom of whether the user (driver) is attempting to switch from acceleration to deceleration or from deceleration to acceleration can be detected in advance, fuel efficiency can be improved without impairing drivability.
Exemplary references include: JP5-33714A; JP2006-232033A; JP6-117528A; JP2001-183150A; JP2005-168174A; JP2007-155643A; G. Seni and J. Elder, “Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions”, Morgan and Claypool Publishers, 2010, and L. I. Kuncheva, “Combining Pattern Classifiers: Methods and Algorithms”, Willy-Interscience, 2004.